For Participants

Misc

Please allow 10-15 minutes to walk to the Resort from downtown.
To get there, walk
away from the coast up to Tremont St, which runs East-West.
Go to the
Catalina Country Club at the 5-way intersection.
Continue up Country Club Drive for about 5 minutes, past
the Country Club building,
until you see the Canyon Resort on your right.
Once there, follow signs to the Catalina Room on the second floor,
past the swimming pool.
If you are unable to walk uphill, please call for a taxi
on 310-510-0025.

From high-throughput biology to
astronomy to financial markets, a wide
variety of complex high-dimensional
domains are inherently continuous.
The statistical copula framework is a
powerful mechanism for constructing
multivariate real-valued distributions.
The essence of the framework is that it
allows us to separate the choice of the
univariate marginals from that of the
dependency structure, as captured by the
copula function. This provides great
modeling flexibility that often leads to
substantial quantitative and qualitative
advantages. Indeed, there has been a
dramatic growth of academic and practical
interest in copulas in recent years, with
applications ranging from mainstream
economics to hydrologic flood analysis.
Copulas have even been famously accused
of "bringing the world financial system
to its knees" (Wired Magazine, 2009).
Yet, the study and use of copulas for
high dimensional data is still in its
infancy.

While studied in statistics for many
years, it is somewhat remarkable that the
general purpose "distribution
generating" framework of copulas was
only recently noticed by machine learning
researchers in general and the
probabilistic graphical models community
in particular. Accordingly, the first
part of the tutorial aims to draw the
attention of the community to this
important framework and will cover: (i) a
motivation and introduction to copulas;
(ii) copulas as flexible multivariate
models and dependence functions; (iii)
copula models: examples, properties,
advantages, visualization; (iv) inference
for copula models; (v) copula
constructions. The second part of the
tutorial will provide a partial survey of
recent copula-based works in machine
learning as a teaser for further
research.

Biographical details

Gal Elidan is a
faculty member of the Statistics
Department at the Hebrew University of
Jerusalem. He received his Ph.D. from the
Hebrew University in 2005 under the
supervision of Prof. Nir Friedman. Prior
to the Hebrew University, he was a
postdoctoral scholar in Prof. Daphne
Koller's machine learning group in
Stanford University. His research
interests include representation,
inference and in particular structure
learning of probabilistic graphical
models and their application to
bioinformatics, machine vision and
medical diagnosis. His most recent works
focus on coping with complex,
non-Gaussian and multimodal domains via
copula-based representations.

Many real-world problems involve negative
interactions; we might want search
results to be diverse, sentences in a
summary to cover distinct aspects of the
subject, or objects in an image to occupy
different regions of space.
However, traditional structured
probabilistic models tend deal poorly
with these kinds of situations; Markov
random fields, for example, become
intractable even to approximate.

In this tutorial we will define and
describe determinantal point processes
(DPPs), which behave in a complementary
fashion: while they cannot encode
positive interactions, they define
expressive models of negative
correlations that come with surprising
and elegant algorithms for many types of
inference, including normalization,
conditioning, marginalization, and
sampling. While DPPs have been
studied by mathematicians for over 35
years and play an important role in
random matrix theory, we will show how
they can also be used as models for
real-world data.

Our goal in this tutorial is to translate
the technically dense mathematical
literature and provide the audience with
a comprehensible, intuitive, and
practical introduction to DPPs. We will
also summarize some of the ways in which
recent work in machine learning has made
use of DPPs for modeling real data,
including learning, and discuss
connections to other topics of interest
to the community like spectral
clustering, compressive sensing, and
submodular functions.
Biographical details:

Alex Kulesza is
a PhD student at the University of
Pennsylvania's Department of Computer and
Information Science, advised by Fernando
Pereira and Ben Taskar. His primary
research interests are in machine
learning algorithms and theory,
particularly structured models. He
has worked on applications in natural
language processing, computer vision, and
computational finance.

Ben Taskar
received his bachelor's and doctoral
degree in Computer Science from Stanford
University. After a postdoc at the
University of California at Berkeley, he
joined the faculty at the University of
Pennsylvania Computer and Information
Science Department in 2007, where he
currently co-directs PRiML: Penn Research
in Machine Learning. His research
interests include machine learning,
natural language processing and computer
vision. He has been awarded the Sloan
Research Fellowship, the NSF CAREER
Award, and selected for the Young
Investigator Program by the Office of
Naval Research and the DARPA Computer
Science Study Group. His work on
structured prediction has received best
paper awards at NIPS and EMNLP
conferences. He previously
presented tutorials at NIPS 2007, UAI
2005 and ACL 2005.

Recent years have witnessed rapid
developments in the field of Causality.
This tutorial focuses on the role of
graphical models in Causal Inferencing
and aims to apprise the participants of
the fundamental ideas as well as the
latest developments in Causality. We
shall describe the use of graphical
models, mostly Directed Acyclic Graphs
(DAGs), as (i) a formal language for
expressing cause-effect relationships and
(ii) a mathematical tool for predicting
the effect of actions/policies. We will
demonstrate the primacy of causal over
probabilistic models and cover in depth
the conditions that insure
identifiability of causal effects in
semi-Markovian models. Finally we shall
discuss cutting-edge algorithms for
finding minimal separators and finding
Markov equivalence. Topics covered would
include interventions, identifiability,
counterfactuals and maximal ancestral
graphs among others.

Biographical details:

Karthika Mohan is a PhD student in
Computer Science at the University of
California, Los Angeles (UCLA). She is
advised by Prof. Judea Pearl and is a
member of the Cognitive Systems
Laboratory at UCLA. Her areas of interest
include causal inference, probabilistic
reasoning, graphical models and transfer
learning. Prior to joining UCLA she was a
graduate student at the International
Institute of Information Technology -
Hyderabad (IIIT-H), India, where she
primarily worked on Speech-to-speech
translation systems and Indic Language
OCRs.

Judea Pearl is
a professor of computer science and
statistics at University of California,
Los Angeles, where he directs the
Cognitive Systems Laboratory and conducts
research in artificial intelligence,
human reasoning and philosophy of
science. He authored three books
including Causality (Cambridge University
Press, 2000, 2009) which pioneered many
developments in causal
reasoning.

Many real-world systems evolve in
continuous-time. Events are not
regulated by a global clock, but rather
proceed asynchronously. Although
theory, algorithms, and applications of
discrete-time Markov models are
relatively wide-spread in artificial
intelligence, their counterparts in
continuous-time have received much less
attention.

This tutorial will introduce
continuous-time Markov processes.
The first part will provide the
mathematical background. We will
discuss their parameterization,
semantics, and basic learning and
inference procedures. We will also
discuss their relative advantages
compared with discrete-time Markov
processes.

The second section will cover
decision-diagram-based compact
representations of continuous-time
processes. These representations
have grown out of the queueing theory and
verification literatures. We will present
factored representations based on
Kronecker algebra, matrix diagrams, and
edge-valued decision diagrams, paying
particular attention to their efficient
algorithmic manipulation and use.

The third section will cover
continuous-time Bayesian networks, a
representation of continuous-time
processes analogous to dynamic Bayesian
networks for discrete time. We will
present their representation, parameter
estimation, and inference, focusing on
how such calculations can be more
efficient than in the discrete-time
case.

Biographical details:

Christian Shelton
is an Associate Professor of Computer
Science and Engineering at the University
of California, Riverside. His
research interests are in machine
learning, particularly dynamic systems.
He received his BS from Stanford and his
PhD from MIT. He returned to
Stanford for his post-doc work. He
has been a faculty member at UC Riverside
since 2003. He was the managing
editor of JMLR from 2003 through 2008,
currently serves on the Editorial Board
of JAIR, and is the lead programmer for
CTBN-RLE
(http://rlair.cs.ucr.edu/ctbnrle/), a
library for continuous-time Bayesian
network representations and
algorithms.

Gianfranco Ciardo
is a Professor in the Department of
Computer Science and Engineering at the
University of California,
Riverside. Previously, he has been
on the faculty at the College of William
and Mary, Williamsburg, Virginia, a
Visiting Professor at the University of
Torino, Italy, and at the Technical
University of Berlin, Germany, and has
held research positions at HP Labs (Palo
Alto, California), ICASE (NASA Langley
Research Center, Hampton, Virginia),
Software Productivity Consortium
(Herndon, Virginia), and CSELT (Torino,
Italy). He received a Laurea from
the University of Torino, Italy, and a
PhD from Duke University. He has been on
the editorial board of IEEE Transactions
on Software Engineering and is on the
editorial board of Transactions on Petri
Nets and Other Models of
Concurrency. He was keynote speaker
at PNPM'01, ATPN'04, EPEW/WS-FM'05, and
PDMC 2009. He is interested in
algorithms and tools for logic and
stochastic analysis of discrete-state
models, symbolic model checking,
performance and reliability evaluation of
complex hardware/software systems, Petri
nets, and Markov +models.